Reasons organizations must invest in Data engineering and MLOps talents

The global big data and data engineering services market is expected to grow from $29.50 billion in 2017 to $77.37 billion by 2023, at a compound annual growth rate (CAGR) of 17.6% during the forecast period, according to a study by MarketsandMarkets. The base year for this study is 2017, and the forecast period is 2018–2023.

Data is the focus for most organizations, hence no doubt, Data Engineering, Data Science, and Machine Learning Operations (MLOps) are the emerging roles of the future. The trend is not just restricted to IT companies. Businesses that need to be fail-proof including the financial industry, travel, food and beverages, leisure, hospitality, and transportation are at the forefront of adopting data practices to build intelligent business models. 

MLOps is a complex puzzle and Data Engineering is one part of it, while Artificial Intelligence (AI), Machine Learning (ML), Data Science, and DevOps are other parts. In this article, we will limit the scope of Data Engineering and MLOps as the fast-emerging fields of Software Development.  

The Role and Growth of Data Engineer 

Data engineering is one of the most sought skills in the technology market and with an expansion in the use of mobile devices, Artificial Intelligence, and the internet of things, it is bound to increase. 

Data engineering and data science are correlated, and data engineering provides the pathway to becoming a data scientist; the latter is the one analysing a large set of data and determining actionable patterns. Data engineers focus on preparing the data - structured and unstructured - from multiple sources. The scientists use the data to build models on top of which business or operational models can be built. The role of data engineering is to collect, maintain and clean up data and then progress to data analyst careers focussed on statistical analysis, data management, and data modelling.

The path to become Chief Digital Officer (CDO)

In a future-focused organization, data analysts work with business analysts and are involved in analysing systems and the evolution of those systems using the data lens. The data has to fit into business analysis and engineers can move into analyst roles in data itself or move sideways into business analyst roles. They can then move to the functional side as architect/ technology leaders and finally become the CDO, which is a new trend in corporations. The career path leads straight to the C-suite. The CDO focused on analyzing large sets of internal data of the company as well as market data, works very closely with the CEO to provide strategic direction in terms of business, revenue, and operational efficiency of the company. 

Let’s talk about MLOps

In 2021, accelerating digital transformation projects has become the priority for organizations to enable the new normal of work following pandemic protocols. The new normal requires businesses to build systems and applications that are highly intelligent, self-learning, and self-healing. MLOps is fundamental to achieve this. MLOps, a set of practices that combines machine learning, DevOps, Data Science, and Data Engineering, aims to deploy and maintain ML systems in production reliably and efficiently.

MLOps is the way ahead for businesses to unlock tons of untapped data, save time, and reduce manpower costs, and building more fluid operations, intelligent decision-making systems, and a more responsive customer experience. As MLOps and AI will become more affordable and configurable, this means companies will need more talent to run and manage these systems, making it another hot skill in the technology market. 

MLOps borrow principles from DevOps. To manifest MLOps in practice, data scientists must work closely with data engineering and ITOps. This requires a lot of collaboration and communication between different teams and disciplines. MLOps streamlines the whole process of data collection, cleaning, streamlining, building models, and put them into production removing workgroup siloes.  

Regarding MLOps future, there will be less need for the mundane bottom-level data cleaning up operations. Instead, investment in skillsets is likely to go into the building models and setting up MLOps pipelines. While skills and training in clean data, big size data, and analyzing large data sets will be required such as PL/SQL, python statistics, and linear regression it is important for engineers and analysts, moving up the hierarchy to the architect level to perfect the visualization. Comfort with visualization technology such as Snowflake and Grafana is important.

Data Engineering and MLOps talent is crucial for future success 

As organizations keep collecting Data from multiple sources, it is essential to put it to use and compete in the future with data as a base to improve Customer Experience, streamlining Engineering Operations, and enhance Decision-Models. This makes Data Engineering and MLOps the two critical pieces for businesses. It is the right time for businesses to invest in these talents and provide them enough opportunities to build futuristic systems. 

The article is authored by Ananth Vinnakota, Sr. VP– Delivery and Product Engineering, Qentelli